Shape-aware MLS deformation for line handles

Ojaswa Sharma, Ranjith Tharayil
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Abstract

In this work, we propose a shape-aware extension to the Moving Least Squares (MLS) image deformation algorithm proposed by Schaefer et al. The original algorithm is not applicable to concave shapes since the weights are based on the Euclidean distance. A number of recent MLS-based deformation algorithms have proposed shape-aware extensions, but only for point handles. In contrast, we suggest a closed-form mathematical solution that applies naturally to point handles as well as line handles. In particular, our solution is based on the interior distance metric to the MLS weights for line handles so that they are shape aware and produce plausible deformations.
线条手柄的形状感知MLS变形
在这项工作中,我们对Schaefer等人提出的移动最小二乘(MLS)图像变形算法进行了形状感知扩展。由于原算法的权值是基于欧几里德距离的,因此不适用于凹形状。最近一些基于mls的变形算法提出了形状感知扩展,但仅针对点句柄。相反,我们建议一个封闭形式的数学解决方案,自然适用于点句柄和线句柄。特别是,我们的解决方案基于线柄的MLS权重的内部距离度量,因此它们具有形状感知并产生合理的变形。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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